Speaker 1In my opinion, even the labs in China that are building AI right now, I would classify them as good guys. I've never hidden from my employees that there will be a transformation. Jensen is bound by the success of open source. Models are interchangeable, but the workflow, the map of work and the workflows around the map of work is where the real value is. This is 20VC with me, Harry
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Speaker 1thank you so much for joining me, dude. Likewise, dude. It's always a pleasure to be here. And it's I think the hottest moment in technology. So I'm very excited to talk to you and get your perspective also on a lot of topics.
Speaker 2It really is the most wild time right now. And so I want to start, you've written a book. A lot of people write books. With the greatest of respects, you're my friend and I care deeply about you. Why on earth did you decide to write a book as a public company CEO? No offense,
Speaker 3you're not doing it for the royalties. Why did you decide to write a book?
Speaker 1Man, I dreamt to write a book since I was a kid. And I discovered I have no talent. I got really a great opportunity. Claude and Chad GPT helped me a lot. They were my ghostwriters. And it was a good moment actually to put my own ideas in order. Because really, when you write something, you get much more clear perspectives on what you are doing. So it was maybe almost six months effort. And I started with different threads of thought. One was, you know, what are the limitations of AI? Is there any durable limitation of AI? Or in a couple of years, there will be millions of Einsteins in the data center and we can all go to play or do whatever we would like to do because
Speaker 2the Einsteins will do the work for us. Can we just start on that then? I think it's nice to take it in kind of segments. Limitations of AI, Einsteins in data centers. How should we be thinking about that moving forwards?
Speaker 1When I heard about this statement that in a couple of years, we will have millions of Einsteins in the data center. Look, I was really concerned. And Dario is a guy that I highly respect and he's highly successful. So I was thinking, man, what does it mean for us? What does it mean for, can I hire one of these Einsteins, put it in a laptop somehow, assign, you know, an enterprise account, a Slack account, and I ask, do my job or do whatever job in the enterprise? It seems that reality is maybe different. And probably Dario wanted to say that we'll have millions of entities that will have the, some of the reasoning powers of Einstein, which I agree, but not Einsteins as a persons, not Einsteins that are capable of learning on the job. Because do you agree that one of the major expectation when you hire someone is that they will hire on the job? There is no manual that a company has to give a new employee. This is exactly how you do your job from end to end. So we expect that.
Speaker 2No, I think humans do learn on the job and they do improve on the job as do models. So similar there, except humans get tired. Humans want more money. Humans want culture. Humans can be toxic. Humans are difficult to manage. I'll take the AI any day of the week, please.
Speaker 1If AI can work as well as a human, Harry, but you say that AI learn on the job. AI can create a notepad on the job, a scratch pad where they can memorize some of the policies on the job. But AI doesn't alter its weights on the job in the way humans are transformed by a job. This is a huge difference. Let me give you an example. You have two chefs. One chef 20 years has done only Japanese food, the other only Italian food. And you give them one recipe. They will create different foods. So it's not that you can write down your enterprise on a sheet of paper. It's much more complex. It's becoming. It's like Think about if I give to someone the ability to read all the books about chess, do you think he will become a grandmaster without playing, without losing, without going through all of this process? Probably not. If I have you watch all the videos about skiing, are you becoming a skier? No, you are not becoming a skier.
Speaker 2But I think it depends what task and workflow you're doing within the enterprise. If we look at the majority of actually what the people within UiPath and every company do, whether it's accounting and finance, whether it's marketing and sales, whether largely outbound and inbound, but whether it's social media. And dude, most of this is execution oriented where, yes, judgment and ambiguity and taste is important at the top. But most of what people do is execution.
Speaker 1I disagree with you. I think most of the people will display some sort of micro initiatives during the job. Maybe I have a hunch this customer. Is going to chair and I can act before even any data is coming. How do I develop this hunch? It's through my years of transformation. It's not written on a piece of paper. It's a big difference between writing an operating model on a piece of paper and leaving it. It's almost impossible for an enterprise for AI. Do you admit that everything has to be written down, documented every time I'm asking a question to the model? The model will read my entire enterprise will have to read it. Okay. Yeah, sure. So this is not possible.
Speaker 2But I also think you're talking about, and with the greatest respect, today's state of AI. I think at the pace of AI.
Speaker 1This is one of the biggest bottlenecks right now because AI doesn't train on the job, doesn't train their own weights. Every time I am doing something after this talk with you, I am being transformed. I carry with me this discussion in all my thoughts. This is not true about AI.
Speaker 2Well, I mean, I'm not being rude. It is like that's why people remain with open AI because it has memory and it is able to infer from past queries, prompts, and give you suggestions based on those. So it does have memory.
Speaker 1It has memory, but memory, it's not necessarily learning. It's not the same thing. Memory, it's just a thing that is written down. When I'm talking to you, I don't go back into my memory. I am just being transformed. It's like a model, it's like a new version of the model. model that comes improved. The new version is not the old version plus a piece of paper that has memorized. It's transformed into its own ways. This is why a model becomes much better. Look, I want to give you a simple example using our own technology. We make our UiPath platform available for coding agents, so people are making it much easier to create automations on UiPath right now. But what we discovered is that a model that has read open source technology and they have a lot of examples and they already have in its ways a certain technology will be much better than to create on our own technology. Because regardless how many prompts and skills we create, the model has it in its own ways. It's very different. Think of all the metaphors in the world when you read something versus when you leave something. You can read the biography. It doesn't mean you live that life. It doesn't mean you are transforming. You are going to answer like that person that lived it. To me, really, this is the biggest limitations that the models have right now.
Speaker 2So I actually do agree with you, but I think everyone does. And that's why everyone is chasing recursive self-improvement so much, where it's models that can continuously learn from themselves and improve themselves over time without need for human intervention. Does that not remove the limitation that we just said?
Speaker 1I don't know, Ben. Maybe we are the result of a self-improvement loop. Look, do this thought exercise. Let's put a model that we have today with the best technology, put it in a spaceship and throw it to the stars. Let's say that we have this technology, like I think Von Neumann imagined that is self-replicated. This spaceship goes to different stars, get energy, and they can continue. So compute will be infinite. And models will self-improve. Were they with us? What would we end up with? Maybe they will create a simulation of a world like ourselves, right? Because they will improve infinitely. Basically, this is the theory. So they will simulate a world as complex as our own world within it. But that means that we are part of an infinite simulations. So I don't know where it's going to lead. But I know that there is a big distinction that I made in the book between will and reasoning. It's not like that we are certain that the will to do something emerges from reasoning or even from consciousness. I think will is a kind of a separate part of the fabric of the universe. I don't think we as humanity have clarity about what will is. I think it's wishful thinking to believe that I can take a big model, put into a self-improvement loop, and this model is going to generate will. But I don't kind of believe so.
Speaker 2But I think we don't know. And I think that's what's so kind of challenging about trying to predict what happens. It's like a world of, as I said, recursive self-improvement. It is unknown in terms of where it lands. It's like a technology you can create can become something you didn't know it could be. And I guess for me, the question then is like, we see the news this week, Dario says we need to pace the frontier. You run UiPath today. Do you feel we need to pace the frontier?
Speaker 1I would say if they truly... I really believe that this technology is becoming rogue and they cannot control it. And their experiments will create significant loss for, I don't know, internet, other systems. If I were them, I would pace it at any cost because I don't want to risk going to jail, honestly. I think there are laws that kind of control this type of rogue behavior. So honestly, I don't need an external pressure. To control. I would be just concerned citizen and I would not build a technology that, you know, is causing harm. But now, of course, they think that this is the only way to protect against the bad boys. So we are the good guys, but there will be some bad boys that probably in other parts of the world that will build the technology regardless. So I think that probably they ask more like a pass. I want to build this technology at any risk. And I'm willing to open my gates for other to see. See how I'm doing because I want to do it in as of, you know, like good manner as possible. But in the same time, I want to be free of consequences. To me, I think this is a bit how I read this memo because otherwise I think it's kind of obvious. I don't think we will reason with the bad guys and we can make a coalition with the bad guys to stop the frontier. So we can make a coalition only with the good guys regardless. I would take even in my opinion, even the labs in China that are building AI right now, I would classify them as a good guys. But someone to me, I think the indirect, the attack is probably on open source because basically they say even if the good guys are building open source, but that open source will get into the hands of the bad guys. And this is unknown bad guys. This is the real danger. So the danger is in open source. So indirectly, it's also an attack to open source in this way. It's a way of interpreting, I guess.
Speaker 2You work with some of the biggest enterprises in the world with UiPath. Alex Carpenter said from Palantir that the biggest enterprise in the world is scared to work with frontier labs because of threat of them coming in to their businesses over time. They have the data, they could build their own and compete against them. Do you see large enterprises scared to work with frontier providers?
Speaker 1I think so. Yes. I don't think people are scared that open AI will build a competitive competitor to them necessarily. I don't see this coming. They are more scared that their IP would leak to other existing competitors somehow. Because I don't know if I'm manufacturing somehow shrews or whatever. I don't think open AI is going to come and compete with me on this thing. But probably some of other guys can get indirectly via the models the same intelligence if open AI will train the model. I think that's the real danger. And I think it's a legitimate danger. And I think everyone is trying to protect their IPs.
Speaker 2You said about kind of clear articulation of thoughts that come from writing. What was another thought that you clearly articulated through the writing process?
Speaker 1It has become very clear to me that another limitation of AI is what I call exactness. AI by its nature being probabilistic at every step. In a way it can lose it. While you do 100, 200 steps by AI, even at each step you have 99% probability. For instance, it's 0.99 at power of 100. You will end up with maybe 60% probability to do the entire step. So AI doesn't have this mechanism to follow steps exactly hundreds, millions of time in the same way. You can see it even if you ask AI to multiply very large number of millions of times. At some point they will make a mistake. And also it's surface kind of another simple idea. Even if you have a tool like AI that is capable of doing multiplications, why you are not using a computer that is doing these multiplications millions of times, you know, 100% of time exact. The fact that the tool can do a job doesn't mean you have to use that tool to do that type of job.
Speaker 2Isn't it because it's where you are? And that's the important thing. It's the importance of being in the harness of the workflow, which is like, I get you completely. You could use something else. I'm asking you, I'm not saying. But because you're in ChatGPT continuously every day, instead of switching to a computer or calculator or whatever, you ask what you're already in, the importance of the environment.
Speaker 1ChatGPT acts like an interface to convert my questions in natural language into exactness. But the exactness is not run by... ChatGPT exactness is run by a computer. Because even today, if you ask ChatGPT, please multiply these two big numbers, they are using behind the scene a tool, a computer, and they will give you the exact number. This is part of the power of the models. So you actually can see on the desktop level, on this type of core work or ChatGPT work, you see the capability to call tools. And what I am saying, you extend this capability to the enterprise. When everything that should be exact, should run on exact technologies. There is no point to run it on probabilistic technologies. And here comes, I think, the most interesting thing. There isn't asymmetry in the deployment of AI and automation in an enterprise. Deploying AI agents is not getting easier today than it was two years ago, in my opinion. But deploying automation has become much easier. Because I can create these automations with AI, with coding agents. Coding agents has been the most major giant leap that we were seeing in the past year. I would say since the invention of ChatGPT, then Chain of Thoughts, and then coding agents were the major milestones. So with coding agents that act in design time, when I build the systems, I can create automations exactness every time during the execution time. So this is the symmetry that is happening right now. Plus, when an automation breaks, because any change in the upstream system, AI comes again into play and fix the automation itself. So this is really the pattern that we are seeing emerging in an enterprise. And this is that AI, it's actually creating the software that runs an enterprise. And this software cannot behave wrong, because this software cannot change its behavior in real time. It's not a probabilistic technology. Even if the software is created by AI, we can really audit, we can have humans that read it, validate it. I can have tests that, you know, for a certain input will guarantee that the software will behave in the same way. So that's, again, makes this pattern extremely powerful. You create, you use AI to create software that runs the enterprise in a predictable, government, auditable.
Speaker 2How many engineers do you have today?
Speaker 1Maybe more than a thousand.
Speaker 2More than a thousand engineers? Yeah. How many people do you have? Around 4,000. You have 4,000 people? Yes. Why so, right? I find 18 a fucking nightmare. What? 4,000? Oh my God. Do you have too many?
Speaker 1It's a complicated question, because I think I would answer more with the thing that is in my book. I think the more we transform, our companies using AI, we have to, in the same time, to transform our workforce. I've never hidden from my employees that there will be a transformation in the company. But I told them upfront, guys, we are not doing anything stupid. We are not just using AI as a pretext to cut a part of the company. We need to do the transformation in the same time as we successfully adopt AI in an enterprise. That's actually another point that I discovered writing this book. If you look, I was looking deeply at jobs and what jobs can be affected by AI, what jobs can be enhanced, and how this transformation is going to look like. One of the things that seems very simple in retrospect is that people don't have very simple jobs that, again, can be defined on a sheet of paper. Every job has some kind of... outcome that is measurable, and this is the outcome that people are hired for. But there is another outcome that is part of the institutional strength. Think about my deep relationship with the customer. It's not necessarily part of the numbers that I'm producing, but it's maybe what makes this customer sticking to my technology. This is a different outcome. If I'm going blindly and I cut on a number of A's, and say, because AI is going to replace them, AI can call the customers and write emails, I don't think AI can supplement the human connections and the trust. So this is a different outcome of the job. And it reflects on every employee, on every type of role in the company. So to me, an enterprise should have a ledger where they actually understand what people are doing besides their main definition of the role. And only after they have this ledger and understanding, they can look at how does the AI transformation look like? What kind of jobs will be affected? How can I move people maybe from one job to the other? Because they still carry some kind of the cultural aspect of the enterprise. So I don't think it's a simple problem as AI is going to cut 20% of the company, let's do a riff 20% out, and then we increase the AI adoption. I think on the contrary, when you do this blindly, you risk to hallow the enterprise of exactly the same talent that thrives during AI. Because let me give you an example. In the book, I call this credentialed middle, that was the type of people that were prevalent in any enterprises. And if you think of our education system and the way we hire people, we hire based on deep expertise in a particular domain, a credential. And this is exactly the type of expertise that might not be needed so much. AI can really help. So you need a fewer of these experts, but you will need more people that have initiatives that are capable of maintaining a relationship with the customer that can be mentors for new employees that bear the cultural aspect of the enterprise. So it's counterintuitive because you will tend to cut those people that are not the biggest experts in the domain, but you will cut exactly what you will need to bring the AI to supplement these experts.
Speaker 2But maybe you just need less of them. And so, you know, I was speaking to a lawyer today and I said, you know, how big is your trainee program? And they go, well, historically, it was 25. And I said, wow, that's a lot of trainees. And he said, yeah, but this year it'll be four.
Speaker 1I 100% agree, we will need less of the people in probably most of the roles. But the main question is, which ones? How do you choose?
Speaker 2Well, I think you can choose quite simply for where is their verifiability? Finance and accounting. It's quite clear what is right and what is wrong.
Speaker 1I disagree with you. It's not about verifiability. It's about if the work has been defined in a frame set by other people. If the frame is clear, then the AI can understand the frame.
Speaker 2But the frame is clear. Finance and accounting, they do your expenses.
Speaker 1It's not, this is one of the domain where the frame is not clear because when I receive an invoice or I receive an order from a customer, I can treat it differently. There are not always the rules there. I know for this customer, NVIDIA is going to ship with priority to open AI. Maybe they have a rule that says there, yes, my first chips go there. Maybe they don't. That rule they don't. If it's not captured in a frame, AI cannot learn it. This is why you need to create this manual. We call this manual the map of work. You need to hand the map of work to AI in order to be successful.
Speaker 2What do you mean the map of work?
Speaker 1Who basically capture how the work works, how the work happens in an enterprise. This is the map of work. It's all the workflows, all the exceptions, all the procedures, all the systems that you use in order to fulfill a goal of a process.
Speaker 2Sure. Let's say you have a head of finance who sits on top of 30 agents, and exactly when NVIDIA come back and say, "Whoa, whoa, whoa, we're your biggest buyer, and we have special terms on our payments." They go, "Yeah, sure. That's right.
Speaker 1Don't worry about it." But you come to my point. Even in finance, you cannot replace everybody. Not everyone, but you got one person or two people. You'll need a certain number of people. The thing is, if you have X number of people, how do you understand? Which of them stay, and which of them has to go to maybe do different jobs? How do you know? Because ideally, you will get the people that will have literacy in AI that can display initiatives. Because AI cannot exhibit initiative in the human sense. So out of this number of people that you want to keep, you want to keep the people that display the most initiative, even a finance person, treating an invoice with a customer, and then you want to keep the people that have the most initiative, and then you want to keep the people that have the most initiative, and then you want to keep the people that have the most initiative, and then you want to keep the people that have the most initiative, and then you want to keep the people that have the most initiative, and then you want to keep the people that have the most initiative, and then you want to keep the people that have the most initiative, and then you want to keep the people that have the most initiative, and then you want to keep the people that have the most initiative, and then you want to keep the people that have the most initiative, and then you want to keep the people that have the most initiative, and then you want to keep the people that have the most initiative, and then you want to keep the people that have the most initiative, and then you want to keep the people that have the most initiative, and then you want to keep the people that have
Speaker 2the most initiative, and then you want to keep the people that have the most initiative, and then you want to keep the people that have the most initiative, and then you want to keep the people that have the most initiative, and then you want to keep the people that have the most initiative, and then you want to keep the people that have the most initiative, and then you want to keep the people that have the most initiative, and then you want to keep the people that have the most initiative, and then you want to keep the people that have the most initiative, and then you want to keep the people that have the most initiative, and then you want to keep the people that have the most initiative, and then you want to keep the people that have the most initiative, and then you want to keep the people that of a call, the warm text afterwards to a customer, the illegible data. How do we think about capturing the data that shows value that we don't capture? I think this is the crux of the problem.
Speaker 1And here you're a true philosopher. Yeah, yeah. And this is where we put a lot of effort as a company. And we are introducing a new technology that we call it cartography. And cartography is a discipline. So it's a discipline to help companies surface all the information of how the work is done and help them create this map of work. And one big important of cartography is to actually have investigate what people are doing on their desktops. And we have a product that we call the cartographer agent that can interview people, real subject matter experts, have them basically record what they are doing. And the agent is interviewing them in real time. If you interview a finance person, it can ask, why did you change this invoice when the zip code was different? Why did you choose a different path? Tell me. And they can start surfacing all of these exceptions. So that's real agents interviewing real people. It's pretty cool stuff. And then you consolidate data from multiple people. And then we create the process maps. So we show them how the work works in kind of real time. And then after this map of work that showed the work as is, you can use our coding agents and you can come up with an idea of how you should transform the process. And you transform the process by printing software. So you start with the process and at point A, it's kind of fully manual. Of course, you have enterprise systems like system of records, but people operate the system. I think the goal of any enterprise is to have less people operating the system and more automations and agentic AI operating the systems.
Speaker 2Do you not fear pushback from people working in the company, aware that you are watching what they do to replace them? That is what Zuck got.
Speaker 1This is inevitable. It very much depends how you pitch the company. So in UiPath, I think it was important to tell people again, hey, so you're not doing anything stupid. We are not doing any mass extinction, you know, for the pretense of AI, but transformation is inevitable and you guys have to transform and everybody will get a chance. And the people that will become more literate in respect to AI will have a better chance, not only here, but in the future in any other job. That's the message that I think everybody should get.
Speaker 2Did they respond to it? Did you see AI adoption through the roof after that?
Speaker 1I think the response is good. AI adoption. It requires more cycles than just discovering the process and people's input in this. But look, in the beginning of the year, the fear of people across the industry, and not only in my company, but in many companies was off charts. The fear of completely being replaced. Now, I think people start to get a bit more understanding of the durability of their jobs and this AI diffusion in enterprise that can happen. At a slower pace and can happen one process at a time because these millions of Einsteins are not hireable yet.
Speaker 2One of the companies we've invested in, McCore, data provider, Brandon Foody, the CEO tweeted yesterday that they spend 3x the spend of human salaries on inference. What percent or multiple or take would you say you spend human salaries on inference?
Speaker 1I personally don't care about it. Let me tell you something. It's a simple hypothesis. If the work and the quality were better of a human can be done by a machine, I will hire today a machine even if it's more expensive than a human. Because human costs will only increase and they bring errors into the pictures while the cost of machine will increase. So I will have a competitive advantage compared to people that will stick to humans. I think everyone will do this. So I don't think the cost of tokens, it will be the real question if you replace a person with AI. But the real problem? The real problem today is that AI cannot replace a person. Because if, again, if bring me an Einstein that replace me and I will happily go on any vacation in the world. But this Einstein doesn't yet exist. I would like to be interviewed and have this podcast with another Einstein. Man, this thing doesn't exist today. That's the reality. So let's call it a reality. Let's call a spade a spade. Maybe this technology will emerge and somehow Einsteins that embody like a person, that have will. That gets transform on the job, learn on the job, have the capability of reasoning, imagination of Einsteins exist. Of course, all the jobs will go extinct.
Speaker 2I have a show with Jason Lemkin from Sasta. He's cut his team from 25 to 2. If he were in my seat now, he would say, no, no, no, it does. I replaced my VP finance. I replaced my VP marketing. And actually, the AI is better.
Speaker 1I want to see this. I heard companies that replace hundreds of support people in the past, and now they are rehiring these people. I think until this model is proven and is proven at scale, not in a particular industry for a particular guy, I don't think we can we can extrapolate for one data point that is going to go across industry.
Speaker 2You said about extrapolation and over-exaggeration. The SaaSpocalypse was very real. We're going to vibe code everything. Did you vibe code tools out?
Speaker 1Look, it was amazing. Yes, we did. But not to an extraordinary success. Initially, it seemed extraordinary. But when we tried to put it in production, we started to see, you know, some kind of real bottlenecks with these tools. And you need to have a lot of things that you have to maintain connectors, permissions, audit, security, and always taking a software from a prototype to production. It's actually where the work is. It's not necessarily the work. It's the writing code. Writing code is fun, but it's not there where you can really make the difference. I think it's much easier today to make a prototype. Prototype is so easy, but then you have to iterate to make the prototype in production. And this is the testing and everything else. We were trying to replace, like, a procurement tool, writing our set, and I think we have a lot of success. And it was initially, it was written only by AI. But then it comes to, you know, we started to have a lot of problems. But then it comes to, you know, we started to have a lot of problems. But then it comes to, you know, we started to have a lot of problems. But then it comes to, you know, we started to have a lot of problems. But then it comes to, you know, we started to have a lot of problems. So, you know, speaking to this self-improvement loops, kind of don't trust, we don't have enough test, we don't have enough trust to put this tool in production, 100%. And humans, in our experience, humans have to intervene a lot into how this vibe-coded tool. For instance, the database schema that vibe-coded tool created was completely bogus. A human has to come and, you know, create the structure. So, right now, you are not at the point. Where, you know, you will have like a business user that understands a problem and they will vibe-code a tool. So, you will still need to put engineers and people, you need to maintain it. So, it's a long process. So, eventually, you will end up paying probably as much, if not more, as the tool you replace. While you keep some of your good and best people bandwidth occupied. Would you buy Salesforce today? I would buy Salesforce. As a system of record. As a stock. As a stock. Look, I invest in software as a category. And I think I made a good investment in, you know, a few months ago because I bought in the bottom of a subspocalypse. Even our own stock, you know, has been doing better. But the markets, the markets today are driven so much about sentiment and not the value. So, it's kind of hard for me to make a judgment of an individual company. But I don't think Salesforce. Salesforce can be replaced by vibe-coding, if this is the question.
Speaker 2I don't understand why a company would go public today. If you think about the two drivers of being public, number one is liquidity for shareholders, employees and shareholders. Well, Stripe and many companies are able to have liquid stock in private markets. And two is the ability to have M&A, a tradable asset that you can buy with. I mean, many, many private companies are able to buy with private stock. With Stripe, we're going to do PayPal with private stock for $60. So, that's not a barrier. And the casinoization of public markets, as you said, with current stock markets being sentiment driven, I don't understand why one would.
Speaker 1But, Harry, let's don't make a confusion between, you know, some very exceptional companies and the most companies that are out there. There are so many companies that are kind of zombies right now. These 2021 zombies, they would fare better in the public market right now. At least their investors will have a way for. Exiting their employees will have a way to make some money. Nowadays, all of them are sitting on paper money. OK, but public markets will face them with the reality of what's their real valuation. I will not say there is no value in the way the Anthropic is trying to do IPO in the end.
Speaker 2But they're a unique company alongside Openair, which just has to because they have exhausted all private funding that exists. They are literally hitting the tap out. Button on the supply of private capital. They're extraordinary companies because they just need too much money.
Speaker 1Do you believe all the investors in Openair and Anthropic will stay in the companies for years to come?
Speaker 2No, I think some will do, but some will do, of course.
Speaker 1But I think we will see an exodus. Honestly, I don't believe a two trillion or whatever. Maybe they will reach five trillion or because if I buy a two trillion, I need to have a path to five. But if they went public at two trillion, would you sell? Anthropic in particular. I wouldn't. Because do you remember on our last podcast, I think you asked me which is the company that I bet on. And I said Anthropic. And Anthropic was worth 60 billion market cap. Maybe I was stupid. I didn't invest.
Speaker 3You would have made more money on that than Salesforce or ServiceNow or whatever.
Speaker 1Yeah, 100%. So I would not sell. And I think it's the same with Openair. I think Openair has catched up quite nicely. And I use interchangeably right now.
Speaker 2I just think we're in a moment. I think we're in a market where the big get bigger and value concentrates more than ever.
Speaker 1But the real question is, Harry, would I buy a two trillion? That's my real question. And right now, I need to see the real numbers to understand if I will go, if I will put money in their IPO.
Speaker 2I'm going to get in so much trouble for this. I think AI is quite like Bitcoin in just the way that it's very difficult to determine what application is going to win, what wallet is going to win, what usage is going to win. And if that is the case. infrastructure that you know is going to be there. And so for me, I think agree with you. I don't know if Claude is going to be better than the next Codex. I don't know if Cursor are going to come out with something fucking amazing. But I do know that Jensen's going to be sitting there going, here's another chip, here's another chip, here's another. Great.
Speaker 1Yeah, but I think Jensen is bound by the success of open source. Because if anthropic and open AI, if this is becoming a dual poly, and basically, I think their time is in like trillions. It's basically the work. They will print their own chips, man. Honestly, it's not such a big deal in the end to print chips. Of course, I mean, open AI doing jalapeno and anthropic are doing their own chips. Exactly. So I don't think Jensen's will be doing so well if they have the single biggest providers and the source of truth and light of God will come from only anthropic and open AI. So therefore, the open source should succeed. I absolutely agree, which is why I think
Speaker 2Jensen is doing the open letter, which everyone signs. Absolutely. In Congress. Encouraging open source.
Speaker 1And I think it was a great move buying... Hugging Face. Hugging Face, yes.
Speaker 2Why?
Speaker 1Because I think it encourages its host all the open source model. It's putting the money where the money is for his company.
Speaker 2Totally. It does also make a neutral provider not neutral anymore. Bias. Yeah, obviously, they have Nematron, and they have their own models now as well. You could say there's a loss of independence now that it's owned by NVIDIA.
Speaker 1I think Nematron is still a small... A small cog in the picture. I think it's valuable, but I would not call it... It's not the same cheap size as the others.
Speaker 2Do you worry about the round-tripping revenue? Everyone talks about NVIDIA investing here, buying here, the circular economy that come from Oracle and open AI. Do you think that's overblown?
Speaker 1It can be, because every major infrastructure in history has been overbuilt. And I think it's a simple explanation, because I was thinking why every infrastructure is over-built. And I think it's a simple explanation, because I was thinking why every infrastructure is over-built. Because you have to make sure you get the biggest piece of the opportunity. If the opportunity is big, it doesn't matter. You build a little bit more than it's necessary. So it's clearly that now everybody... There is only 100% of the pie, and people are building right now 200% of the pie. They will be losers.
Speaker 2Do you not think, though, this is the first innovation where we are significantly underbuilt? And actually, if you look at the constraints... You're right, actually. In prior technology cycles, we overbuilt supply-side, and demand-side was lagging behind. Now we have energy that's being a massive constraint. We have water. We have data centers. We have regulation and policy. We have a significant hindrance to supply-side. And we are underbuilt, not overbuilt, which is why every ounce of compute is taken.
Speaker 1Yes, but are we underbuilt to the extent of this trillions that comes into the infrastructure? Yes, but are we underbuilt to the extent of this trillions that comes into the infrastructure? Everything happens on the premise that AI is going to replace human work in a really large scale. We need to see the timing of this replacement and transformation. It's a big difference if it's coming in 10 years versus next two years. So I don't think it's overbuilt for next decade, but it might be overbuilt for the next three years. And stock markets and capital, you know, can be merciless.
Speaker 2Maybe I'm a childish optimist, but I saw... I saw, you know, Andrej Kapathy say that he used coding tools for 20% of the work. And then six months later, he said that it did 80% of the work and he helped it with 20%. We're investors in Legora. I interviewed lawyers when we did that deal. And they all said to me, "You're such tech bros. You think you can replace us? Haha, we went to law school." And I was like, "Okay, cool." I interviewed them two weeks ago. Every single one of 15 said they would be severely unhappy if it was taken away, with most of them saying they hadn't written a document in six months.
Speaker 1Harry, you should read my book, my friend, because it answers exactly the same questions. When the frame that a person operates is really well defined by someone else, like in law, AI can be devastating in the impact. When the frame is not as clear, and there are so many exceptions that are custom made for an enterprise...
Speaker 2Dude, you're about to spend two days with a lawyer who is my girlfriend. She will tell you that law is highly ambiguous, subjective in terms of writing styles. She would...
Speaker 1So is the human language. And AI understands it perfectly. And it understands every freaking nuance of human kind of sensitivities. As long as it's documented and it's well defined, there is a manual for the freaking law. AI is amazing. When there is no manual, AI is not amazing, and it doesn't work. There's a huge difference.
Speaker 2But $300 billion is the legal industry in the US. It's a lot. If you think about how much labor could be replaced by that, I think 30% would be reasonable. That would be $90 billion of available revenue.
Speaker 1Yeah, but that's not going to convert in token revenue. Maybe out of 90 billion, companies might charge maybe 10%. Maybe it's 10 billion total opportunity in tokens.
Speaker 2Am I mistaken to invest in... Yes, you are mistaken to invest in Legora. And are the Harvey investors mistaken to invest in Harvey if it's 10 billion, not 90 billion?
Speaker 1What I can tell you that from a law perspective, open source models, frontier models will do just fine. Maybe they do also the custom workflows around the legal process, which is really valuable. But to me, that's also a big part of my thesis. Models are interchangeable, but the workflow, the map of work and the workflow... The workflows around the map of work is where the real value is. If Harvey and Legora are doing this, they map really the work, they create the workflow, and they create the legal department for me, of course, it's a much bigger value that they capture. But if it's only to get the legal opinion, a call to a model, that's not going to be a $100 billion market. 100%.
Speaker 2What percent of token traffic do you think will go through open versus closed models?
Speaker 1To me, I think the question is different. What percentage of the traffic will go to truly frontier model like Astra or Fable versus very cost efficient models? For enterprise work, my prediction is that 90% of the flow will go to very cost efficient models. I don't think you need frontier level quality of models for most operational work.
Speaker 2To be clear then, so we will actually still use the core provider, which is open AI and Anthropic. It'll just be deprecated older models.
Speaker 1I will still use Anthropic and open AI. They are cost efficient model, but I will have a verifiable backup on open source all the time. I should be, as a responsible enterprise, I should be able to switch models. I cannot be locked in.
Speaker 2Can I ask you, we invested in... I'm just checking my portfolio against your brain. I believe strongly in open models and I think that every company, not every company, but mid to large scale company will have their own model, own their own intelligence and feed their own data into it. I think it goes to the statement of owning your own intelligence, not renting it. That's why we invested in fireworks. I believe in the open model ecosystem.
Speaker 1Yeah. I'm a big fan of fireworks and we are using them quite a bit. Do you like them? Yes. We like them a lot. I'm a big believer that an enterprise should distribute their intelligence and one of the best should be on open source and very importantly, should be on this map of work. Because think about, if I want to train a model, my own model, with who am I, I need to have this who am I very well documented. I need to create this manual because I'm training one model today, but in the next two months, there is another better model, base model coming into the picture. How can I do transfer learning from my old model into the new model if I don't have the data and the exact manual? I cannot. Or there are terrible losses when I do this. The real investment for an enterprise is to creating this map of work that documents how they actually work. And with this one, they can train their own models. It's in fireworks or other provider, it doesn't matter. But this is their IP and this is their core data. Make sense?
Speaker 2And so you do, sorry, just so I understand, you do believe that companies and a lot of their own models with their own data.
Speaker 1I do believe that they will at least have their own models as a backup to frontier models. To me, where I'm not clear, if I can provide the same cost efficiency with my own model versus a cost efficient model from anthropic open AI, because I think these guys are in the position to truly optimize large infrastructure. So part of their business model will be to deliver, you know, more intelligence per dollar than even I can squeeze from my own models.
Speaker 2If you have highly specific data that is exact to the request that you have, which is your data, I think you'll get more token efficiency with your own model than you wouldn't optimize.
Speaker 1Only if you are training your models, that might be true. And only if you can deliver, if fireworks can deliver at a large scale and in a very optimized way. Would you invest in fireworks? Fireworks at $15 billion? Probably yes, if this hypothesis of open model is true. which I believe is true. I think they are undervalued. I think they will have to get very soon in this big game of securing compute. Because if they don't secure compute, I don't understand how can they give me the inference of the scale that I want. What do you think?
Speaker 2Because you invested in them. I did. I think you're absolutely right that they need to move into the compute layer. And I think Lynn is doing that, I'm sure very soon. So they will have to raise, you know, tens of billions now. And I will be there. And she did that at Facebook. I think that's a unique thing to this team. They did compute securing. It's a great team. We really like them. And we work with them before the big hype. I also think the data providers are massively underpriced and underappreciated. McCore and Surge in particular. Everyone's like, oh, they're commodities. You're just buying data. Data is the most important thing to feed model quality.
Speaker 1But what's the... I think one thing is storage. And one thing is understanding of the data. Because if I have a storage, I can have a tape and I can put data on the tape. Would you invest in a tape company? I don't think so. You need to invest in the intelligence that understands the data and feed the model and, you know, extract the right data at the right time, feed really the model with the data that is needed with the context. Because if you don't have, if you have just data, but you don't have a way to create a really good context, and you don't know how to use the model when they ask something, it's useless.
Speaker 2I think you'd say that they have more data than anyone else across more categories than anyone else. So when the model requests highly specific data, because of the breadth of their library, they're able to provide it in a way that others aren't.
Speaker 1If it's their own data and is valuable for models, I'm sure the models will buy the data in an instant.
Speaker 2Can I ask you, what have you changed your mind on most in the last 12 months?
Speaker 1I didn't understand this necessarily. It's a necessity to have a manual in order to work. That was maybe the biggest breakthrough in my understanding, that every time I'm running a query towards AI, AI should have at their disposition, the entire way, you know, my company work or this particular process work. When I realized this, I understood also that this thing that it's the biggest differential between memory and true learning. We started the discussion and I'm not sure I really make a point, but it's a huge difference between just laying down, having a scratch pad, we're being transformed by an experience. And that's the thing that I realized the most. And I think I realized also what's kind of human for us, because I experienced a lot with AI writing, not code, but writing a book. So I've been through different styles, I understood a lot how to prompt them. AI doesn't have a style, and you realize why they don't have a style, because they are an averager of anything. In order to have a style, you need to have kind of a body, you need to have individuality, because we are the choice that we made and the choice that we don't make, in a sense. So you need to be transformed, because otherwise, I will just ask AI, read this book and write in the spirit of this author, and it's not really working. Because you need to be transformed by the experience. So to me, this is going to be the biggest breakthrough in the AI technology, when I can have models at the size of mythos, being transformed on a job, being, you know, putting in a laptop, and it might be possible, who knows, you know, the pace of technology, maybe 20 years from now, I can have a 10 trillion model that it's my own model and is getting transformed along with me. But we need to see, I think there might be a few series of innovations to get there. Because I want to give you also an interesting data point. AI is solving very interesting math problems that humans didn't solve before, right now. But AI still, it's not capable of creating frameworks. I don't know, relativity is a framework, okay? And I was thinking, why so? I think one of the main reason relates still with this. Not being transformed when you are on a job. When I'm writing a book, I am being transformed by the act of writing this book. Every time I'm writing something down, there is something in me that changes, that is not necessarily the memory thing. It's me that is changing. Einstein has been changed by his experience, thinking about the speed of light of what happens when you go, you know, behind the light. It's not like Einstein wrote it down and then every time he thought again, he rewrote a piece of paper. No, he became gradually, you know, a different Einstein than the one that started to think of a problem when he created this thinking, this frame of relativity. Models don't do this way, even if I put swarm of agents, everything, they have to write down everything. They are not being transformed by the process, so therefore, it's very difficult in the end, they will have this context that one million is very hard to go beyond this one million tokens context window. A frame might require a transformation as you work on that frame. It's a different way of learning than pure memory. So I want to make this argument as clear as possible. Are you optimistic for your children? I'm extremely optimistic for myself, Ari, therefore for my children. I don't want to sound like an AI doomer. Because I believe that- I don't think you do.
Speaker 2I am. No, I don't think you do. I sound like a doomer, in a way. I think we'll have a lot more job loss. I think it will happen a lot quicker. I think we're seeing it in real time.
Speaker 1I'm much more optimistic that we won't have so much. Because based on my own experience with AI, I don't think the diffusion is as fast as you imagine, particularly because enterprise have to document in much greater detail their processes.
Speaker 2Can I just ask, sorry, we're both Europeans and we're both sitting in London. I don't know how to say this, but we don't matter anymore just being blunt. Do you think that gets better or worse in the next three to five years?
Speaker 1Yes, man. It's hard to admit the reality, but from a technology standpoint, I think we are largely irrelevant. But it's so stupid, because the biggest producer of machines that make chips is based in Europe.
Speaker 3It's ASML.
Speaker 1Yeah. We could have made these chips in Europe. You know, some of the most brilliant machines in the world. Yeah. Some of the most brilliant minds that build AI. Even if you think Dario, Sam, all of them are European origins. Ilya, we have the talent, we have the technology to build the machines, but somehow we are losing it. Man, it's very stupid.
Speaker 2Do you see a difference in work ethic having a team in the US and the UK?
Speaker 1Yes. I experience with teams in UK and at 5:00 PM, they are all in the pub.
Speaker 2Why is that? Because money matters more? Yeah.
Speaker 1Because culture matters more than money. It's a more dynamic culture. I don't think I would have succeeded in Europe the way I did in US. So I'm an European, but as an entrepreneur, I'm American. This is what I told to everybody. So my formation is in American school of entrepreneurship, even if I started my company in Europe.
Speaker 2Listen, I get it. And you look at Legora, and you look at Eleven Labs, and some of the best companies to come out of Europe in the last few years. You think the revenue machine is anywhere but in America, you're lying to yourself.
Speaker 1It's an easier to access revenue machine than in Europe, clearly.
Speaker 2Faster, it's easier. The teams have scaled GTM functions before, I completely agree with you.
Speaker 1And American companies are making larger bets on vision without waiting for so many proof points as the European companies. And even people in the middle management can make... Sizable million dollar bets on new technologies in US. I haven't seen this appetite in Europe.
Speaker 2I don't want to ask this, but I am interested. If you were to advise a young European entrepreneur today, would you say to go to the US?
Speaker 1Yes, that's the sad reality. I think that unless they build for a specific market with some specificity in mind, if they build a universal technology, they will have a better chance to succeed in US. There will be many successes. There will be many successful European companies coming out of this. Maybe not as a frontier labs, but I think for the application of AI.
Speaker 2Do you buy sovereignty as an argument?
Speaker 1Yes, I think it's an important one.
Speaker 2The energy sovereignty, model sovereignty.
Speaker 1Yes, 100%. And all European customers right now would prefer on-prem software model sovereignty and model optionality. Yes, 100%. This is a big business that it's coming here. I talked to our friends at Fireworks and I actually tried to convince them to make their software available on-prem. Right now they are in show me the money, but I can tell, guys, this is a big business. You need to prove first, because this is Europe. Show them the technology and the money will come.
Speaker 2How much revenue does UI Parth do today?
Speaker 1I think it's public data. We are at 1.6, growing last year, like 14%.
Speaker 2So Jason Lampkins taught me this. He taught me that, like, unless you're growing 20% plus, you're just fucked in the public markets. It's grow or die. And it's a horrible reality in that way. I'm not condoning it.
Speaker 1horrible. Is that right? Yeah, because I think the public markets are very confused right now with who are the AI winners or losers. And if you don't show serious growth and traction, they automatically put you into AI losers without looking really deep into the business. There are so many hundreds of software companies in the public market. So then it's hard to look at each
Speaker 2of them. So if I were to flip it on you, we do a final one for a quick fire. What is the bull case to UiPath being a $50 billion company? Think about even today, Gartner released their
Speaker 1new boat, Magic Quadrant, business orchestration and automation technologies. We are one of the leaders. We moved from a challenger into a leader in the last year. So it shows that we as a company made this transition from... RPA and automation technology into an orchestration and automation technology. And there are all the arguments in the world that this is really required in order to create this new enterprise that is AI powered. This idea that you can have an AI agent that runs everything for you from top level processes, orchestrate and automate everything by magic. I think it's kind of a thing that people stop believing. You need to have an underpinning orchestration and automation technology and this map of work that I talked in order to power your processes. And this is what we have. And it's not only me saying, but it's Gartner saying, it's Forrester, it's industry analysts in being very bullish on us. So that's really the argument right now. I think, as I said, this asymmetry that AI is creating right now is more and more important. And I think it's a very, very important thing. And I think it's a very, very important thing. And I think it's a very, very important thing. And I think it's a very, very more obvious that printing software that run your processes has become much easier than a year ago. Bringing, creating an AI agent that run your software is as difficult as a year ago. So you make a huge investment into printing this software, capturing the enterprise context that we call the map of work, putting this enterprise context inside these rails that I named this orchestration automation as the map and rails. Map is the context, rails is the orchestration automation. You put them in the same platform, and then you can assign an agent to do work. And you tell the agent, this is the reality. These are the rails you can use. This is the map that describe how to use these rails. This is the goal. And that's the way you can control. You can have a control on the top, but your agents cannot go wrong. No sane enterprise right now will put a swarm of agents and just ask them, do my finance accounting for me. Because who knows, maybe
Speaker 2they will attack your competitor. Send 20 million bucks to some rogue invoice. I completely get you. What is the bear case?
Speaker 1I think the bear case is AI will somehow become genius. Tokens cost will be next to zero. We'll have this literally millions of Einsteins in a data center. But Einsteins in a true sense, not only reasoning, in the sense of replacing a person, and I can assign them to every work in an enterprise, and they will just do it. That's the bear case against us.
Speaker 2I mean, token cost has gone from $60 to $1 per million. So I mean, the token cost will go to nothing.
Speaker 1It's possible. This is why I told you I would not right now stop an investment based on the token cost.
Speaker 2Dude, I could talk to you all day. I'd love to do a quick fire with you. So I say a short statement, and you give me your immediate thoughts, okay? What's the hardest thing about your job today as
Speaker 1CEO of UiPath? It's aligning people. It's so different personalities, pride, ego that comes
Speaker 2into place. This is the hardest. What has changed most about how you work as a CEO because of AI?
Speaker 1I'm spending maybe half of my day right now, half of my day alone with myself in Visual Studio Code right now, working with Claude. I'm spending half of my day working with Claude and ChatGPT. I have way more leverage on my company than before, because we change completely the way we operate. Most of the people, when they come with an idea to me, a year ago, they would come with the deck, and it was very hard even to prepare with this deck. Now, everyone is going to come with the markdown file, and I can put it into, I have a giant strategy folder that, you know, I have, you know, AI agents working with this. I put this document in my folder, and then I can ask intelligent questions.
Speaker 2If you had unlimited resources and zero retribution from Wall Street, what would you do that you're not doing?
Speaker 1Maybe I will try to build my own frontier model.
Speaker 2NVIDIA in three years' time, will it be above $7.5 trillion? It's at 5.6, yeah.
Speaker 1I can easily imagine a 40% run for NVIDIA. I would bet more on NVIDIA rather... Other than Anthropic being a $7 billion company. Brilliant, of course. Billions are nothing today.
Speaker 2Billions are nothing today, yeah. You said something on a show that we did before, and it was like one of the most resonant things I've ever had in a show. And you said, I think a lot of people think they want to be me, but sometimes it's quite lonely, alone in my head. And I always remember this, because I often feel the same. What would you advise to founders who feel lonely in their head and struggle with that today?
Speaker 1I think they should surround themselves with their best friends from maybe childhood, have more frequent chats with them, because they are the people that can relate the most to them before, and they can see them part of the transformation. And it's a nice thing to do. Anyway, you'll still be lonely, but you will have the sense of some kind of continuity in your life. I find one of the most rewarding part of my life, chatting, being with friends, with family. This is really where you get a lot of the loneliness, in a way, you know, a pill from the loneliness.
Speaker 2A final one. What are you most excited for when you look forward? Like, my mother's got MS. I'm really excited for some of the breakthroughs that we'll see with chronic conditions and treatment of them.
Speaker 1Yeah, I'm very excited about longevity. You know, my friend, I have almost twice your age. You're not quite. Any. Dude, are you kidding me? Any kind of gym or anything.
Speaker 2No. What are you doing longevity-wise?
Speaker 1I'm doing quite a lot. I got into, like, peptides, into supplements. I'm taking around 60 different supplements a day and three, four peptides. 60 supplements? Seriously, yeah. 60? 60. Six zero, yeah.
Speaker 3What the fuck are you taking?
Speaker 1And all of them have been recommended and vetted by AI.
Speaker 2What? That's extraordinary. I mean, you look incredibly young, but 60 supplements? Like, in pills? No, you take them in, like, because I do the longevity shake from Brian Johnson, which is, like, 60 in one. And I just have it every morning. You actually have 60 separate supplements.
Speaker 1I have a lot of pills. I also do some in the form of powders. But yeah, I have. I'm going to show you tomorrow all my. I have little bags throughout the day, like, AM1, AM2, AM3.
Speaker 3That's extraordinary. I know. That's extraordinary.
Speaker 1It's very dorky of me, yes.
Speaker 3Do peptides, like, do you feel better?
Speaker 1I think it's supposed to feel in the long term better. But honestly, I feel way better even than 10 years ago. Kind of reducing booze quite a lot helped. And I know you are a big fan of booze. You don't still drink, do you? Do you still drink? I drink a lot less these days.
Speaker 2I love that. Dude, this has been so much fun. Thank you so much for putting up with my meandering. When does the book come out?
Speaker 1It's already available for download. I'm printing also a few copies. We have our... We have our big Fusion event coming in a couple of weeks. And I am distributing to everybody coming their copy.
Speaker 2Dude, this has been a pleasure. I'm going to get a copy. I'm going to get a physical copy because I'm old too. And so I like reading.
Speaker 1It's my gift to you, Harry, of course.
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